Computer networks that facilitate data processing are becoming increasingly common in the modern world. Such networks include multiple nodes, which are typically computers, that may be distributed over vast geographic distances and connected by communications links, such as telephone wires. Each node typically includes a processing element, which processes data, and a communications control unit, which controls the transmission and reception of data in the network across the communications link. The processing element can include one or more processors and memory.
Nodes communicate with each other using packets, which are the basic units of information transfer. A packet contains data surrounded by control and routing information supplied by the various nodes in the network. A message from one node to another may be sent via a single packet, or the node can break the message up into several shorter packets with each packet containing a portion of the message. The communications control unit at a node receives a packet from the communications link and sends the packet to the node's processing element for processing. Likewise, a node's processing element sends a packet to the node's communications control unit, which transmits the packet across the network.
All of this sending, receiving, and processing of packets has an overhead, or cost, associated with it. That is, it takes time to receive a packet at a node, to examine the packet's control information, and to determine what to do next with the packet. One way to reduce the packet overhead is a method that transmits packets in a train, called packet training. This packet training method consolidates individual packets into a group, called a train, which reduces the overhead when compared to processing the same number of packets individually because a node can process the entire train of packets at a single time. The word "train" comes from a train of railroad cars. It is less expensive to form a train of railroad cars pulled by a single locomotive than it is to give each railroad car its own locomotive. Analogously, processing a train of packets has less overhead than processing each packet individually.
In a typical training method, a node will accumulate packets until the train reaches a fixed target length. Then the node will process or retransmit the entire train at once. In order to ensure that the accumulated packets are eventually handled since the packet arrival rate at the node is unpredictable, the method will typically start a timer when node receives the first packet in the train. When the timer expires, the node will conclude the train and process it, even if train has not reached its target length.
This method works well in times of heavy packet traffic because the timer never expires. But in times of light packet traffic, the packets that the node does receive experience poor performance while waiting in vain for additional packets to accumulate, and the ultimate timer expiration introduces additional processing overhead.
Thus, there is a need for a packet training mechanism that will overcome the disadvantages of the prior all and provide improved performance even in times of a light, variable, or unpredictable packet traffic rate.